Increased risk of venous thromboembolism in patients with granulomatosis with polyangiitis: A population-based study
Bibliographic record
Abstract
We assessed the risk and time trends of venous thromboembolism (VTE) including pulmonary embolism (PE) and deep venous thrombosis (DVT) in new granulomatosis with polyangiitis (GPA) cases compared to the general population. Using a population-level database from the entire province of British Columbia, Canada, we conducted a matched cohort study of all patients with incident GPA with up to ten age-, sex-, and entry time-matched individuals randomly selected from the general population. We compared incidence rates of VTE, PE, and DVT between the two groups, and calculated hazard ratios (HR), adjusting for relevant confounders. Among 549 individuals with incident GPA (57.6% female, mean age 55.4 years), the incidence rates for VTE, PE, and DVT were 7.22, 2.73, and 6.32 per 1,000 person-years, respectively; the corresponding rates were 1.36, 0.74, and 0.81 per 1,000 person-years among the 5,490 non-GPA individuals. Compared with the non-GPA cohort, the fully adjusted HRs among GPA patients were 2.90 (95% CI, 1.10-7.64), 4.70 (95% CI, 1.74-12.69), and 1.66 (95% CI, 0.52-5.27) for VTE, PE, and DVT, respectively. The risks of VTE, PE, and DVT were highest during the first year after GPA diagnosis with HR (95% CI) of 11.04 (1.37-88.72), 26.94 (4.56-159.24), and 2.68 (0.23-31.21), respectively. GPA patients are at significantly increased risk of PE, but not DVT. Monitoring for these complications is particularly warranted in this patient population, especially early after diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".